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In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. The forecasting model is obtained through stepwise regression on these components. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The proposed model is conceptually simple and highly accurate. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station. [ABSTRACT FROM AUTHOR]

In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. The forecasting model is obtained through stepwise regression on these components. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The proposed model is conceptually simple and highly accurate. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station.

The article presents the results of an assessment of the intra-annual runoff distribution of the northern slope Zhetysu Alatau rivers. Based on the data of recent years, the average monthly flow of rivers for different years of water content has been clarified, and their changes relative to earlier assessments are shown. The runoff series data of the hydrometeorological service network contain a large number of gaps in observations, in connection with that the hydrological series were reconstructed using the analogy and ratio method. As a validity control of the reconstruction of the series, the root-mean-square errors of the reconstructed series of monthly averages were calculated. To assess the intra-annual runoff distribution of the northern slope Zhetysu Alatau rivers, histograms of the distribution of runoff by months and seasons (as a percentage of the annual) were constructed for four water content groups: high-water, medium, low-water and very low-water for the period 1965-2019. A comparative analysis of the obtained results with the results of previous works, in particular, with the data of «Surface Water Resources of the USSR», where the average periods of the water regime phases are given according to data for 1930-1965 was carried out. The results of the analysis of indicators of the intra-annual runoff distribution in the region under consideration made it possible to identify characteristic features in runoff formation, changes relative to earlier assessments. [ABSTRACT FROM AUTHOR]

• Integrated multi-source satellite data and stochastic modeling for Lower Yellow River planform evolution. • Forecasted long-term flood period channel adjustments to reservoir operations using AI methods. • Quantified spatially differentiated channel migration dynamics and informing adaptive management. • Developed a generalizable methodological framework for studying human-nature fluvial dynamics. • Identified priority locations for channel training works based on morphological evolution predictions. This study investigates the stable diffusion-evolution tendencies of the Lower Yellow River (LYR) corridor during floods in the 21st century following Xiaolangdi Reservoir's operation. While previous studies have assessed short-term, cross-sectional channel adjustments in the LYR, our understanding remains limited regarding the long-term, centennial-scale planform morphological responses to Xiaolangdi Reservoir's operation, as field data are limited and multi-spatiotemporal information is difficult to obtain. We integrate multi-source satellite data to extract typical river morphology information. A stochastic channel-forming relationship model based on hydraulic geometry theory links water–sediment inputs with channel adjustments. The data-model integration effectively captures the dynamics of key channel morphology variables. Long-term forecasts of flood period flows and incoming sediment loads are generated using a GradientBoosted Long Short-Term Memory (LSTM) machine learning approach. The channel centerline evolution is numerically simulated by incorporating the forecasted inputs into a physically based meander migration model and representatively analyzed through clustering and gradient descent regression after Monte Carlo simulation. The spatially differentiated analysis and quantification of channel evolution dynamics fill existing knowledge gaps by providing important insights for developing adaptive channel maintenance and corridor management strategies. The integrated methodologies advance understanding of river channel dynamics under complex human-nature interactions in large plains watersheds. [ABSTRACT FROM AUTHOR]

Floods increase the similarity of the abiotic water characteristics of the rivers with those of the surrounding floodplains and are the main factors that influence the ecosystem dynamics. The aim of this paper was to examine the alterations in abiotic characteristics of the Paranapanema River and three lateral lagoons with different degrees of connectivity to the river during the flood period. Samplings were performed twice a week during a three-month period. Water quality in the Camargo and Coqueiral lagoons, connected to the Paranapanema River, presented patterns of variation similar to those of the lotic ecosystem, evidenced by the principal component analysis. In Cavalos Lagoon, changes in water quality were observed in all the environments, such as a function of dilution after the water level increased and greater nutrients resulting from littoral plant decomposition after submersion. In conclusion, the marginal lagoons and river were influenced by two anthropogenic actions: water storage in a dam reservoir, which acted like a buffer against hydrological pulses, and the widening of the channel uniting Camargo Lagoon with the river, changing the connectivity level and causing an ever-greater similarity of the lagoon with the lotic system.Las inundaciones asemejan las características abióticas del agua de los ríos a la de los entornos de planicies aluviales y son los principales factores que influyen en la dinámica del ecosistema. El objetivo de este trabajo fue analizar las alteraciones en las características abióticas del río Paranapanema y de tres lagunas laterales con distintos niveles de conectividad al río durante el período de inundación. Los muestreos se realizaron dos veces por semana durante un período de tres meses. La calidad del agua en las lagunas Camargo y Coqueiral, conectadas al río Paranapanema, presentó patrones de variación similar a aquellos del ecosistema lótico, demostrado por medio de análisis de componentes principales. En la laguna de Cavalos se observaron cambios en la calidad del agua de todos los ambientes, tales como una función de dilución después del aumento del nivel de agua y del incremento de nutrientes como resultado de la descomposición de las plantas litorales después de la inmersión. En conclusión, las lagunas marginales y el río fueron influenciados por dos acciones antropogénicas: el almacenamiento de agua en la presa, que actúa como un sistema de amortiguación de pulsos hidrológicos y la ampliación del canal de la asociación de la laguna Camargo con el río, cambiando el nivel de conectividad, y causando una similitud cada vez mayor de la laguna con el sistema lóticos.

The objectives of this study were to investigate the levels, dispersion patterns, seasonal variation, and sources of 16 priority polycyclic aromatic hydrocarbons (16 EPA-PAHs) in the Hun River of Liaoning Province, China. Samples of surface water were collected from upstream to downstream locations, and also from the main tributaries of the Hun River in dry period, flood period, and level period, respectively. After appropriate preparation, all samples were analyzed for 16 EPA-PAHs. Total PAHs concentrations varied from 124.55 to 439.27 ng l in surface water in dry period, 1,615.75 to 5,270.04 ng l in flood period, and 2,247.42 to 7,767.9 ng l in level period. The 16 EPA-PAHs concentrations were significantly increased in the order of level period > flood period > dry period. The composition pattern of PAHs in surface water was dominated by low molecular weight PAHs, in particular two- to three-ring PAHs. In addition, two-ring PAH accounted for 39.33 to 88.27 % of the total PAHs in level period. Low molecular weight PAHs predomination together with higher levels of PAHs in flood and level period suggested a relatively recent local source of PAHs. Special PAHs ratios such as phenanthrene/anthracene and fluoranthene/pyrene indicated that under dry weather season conditions, the PAHs found in surface water were primarily from petrogenic source, while under wet weather season conditions they were from mixed source of both petrogenic inputs and combustion sources. The comparison of PAHs contamination among different types of areas in China suggested that atmospheric depositions might be the most important approaches of PAHs into water system. Although the Hun River exists low PAHs ecological risk now, potential toxic effects will be existed in the future especially in flood and level period. [ABSTRACT FROM AUTHOR]

Floods increase the similarity of the abiotic water characteristics of the rivers with those of the surrounding floodplains and are the main factors that influence the ecosystem dynamics. The aim of this paper was to examine the alterations in abiotic characteristics of the Paranapanema River and three lateral lagoons with different degrees of connectivity to the river during the flood period. Samplings were performed twice a week during a three-month period. Water quality in the Camargo and Coqueiral lagoons, connected to the Paranapanema River, presented patterns of variation similar to those of the lotic ecosystem, evidenced by the principal component analysis. In Cavalos Lagoon, changes in water quality were observed in all the environments, such as a function of dilution after the water level increased and greater nutrients resulting from littoral plant decomposition after submersion. In conclusion, the marginal lagoons and river were influenced by two anthropogenic actions: water storage in a dam reservoir, which acted like a buffer against hydrological pulses, and the widening of the channel uniting Camargo Lagoon with the river, changing the connectivity level and causing an ever-greater similarity of the lagoon with the lotic system. [ABSTRACT FROM AUTHOR]

Abstract: The application of artificial neural network (ANN) to rainfall-runoff simulations has provided promising results in recent years. However, it is difficult to obtain satisfying results by using raw data for the direct prediction of the time series of streamflows. To improve simulating daily streamflow with back-propagation (BP) neural networks, the whole data set in this study is divided into two independent groups, flood period and non-flood period. The approaches and techniques of applying the division-based BP (DBP) in runoff simulation are presented in this paper. A comparison of the DBP model to the primitive BP model and the Xinanjiang model was also conducted to evaluate the effectiveness of the improvement. The numerical experimental results indicate that DBP model still overestimated flow peak, but improved considerably the streamflow simulation in the non-flood period. [Copyright &y& Elsevier]

A daily paddy water balance model was developed using four years (1996–1997 and 2001–2002) field experimentals data of two locations. The model could simulate the actual daily ponding depth of paddy pretty well by treating paddy levee height and threshold ponding depth indicating irrigation time as ten days average parameters of the model with 0.76 four year average Nash-Sutcliffe efficiency. The amount ranging from 425.1 mm in 1996 to 850.8 mm in 2002. The storage ratio (amount of storage divided by rainfall) linearly decreased as total rainfall amount increased within the range of observed rainfall. From the model application, direct seeding type was more favorable to store rainfall than transplanting type. [ABSTRACT FROM AUTHOR]